Skip to content
This is a started/demo code for Zero-Shot-Learning via implementation of Embarrassingly simple ZSL (ICML 2015)
Jupyter Notebook Python
Branch: master
Clone or download
Type Name Latest commit message Commit time
Failed to load latest commit information.
.ipynb_checkpoints python code Feb 22, 2019 update Feb 22, 2019
demo_eszsl.ipynb python code Feb 22, 2019 python code Feb 22, 2019

Embarrsingly simple zero-shot learning

This is the implementation of the paper "An embarrassingly simple approach to zero-shot learning." (EsZsl) ICML, [pdf].

The file demo_eszsl is a jupyter notebook which contains a walk through of EsZsl.


The dataset splits can be downloaded here, please download the Proposed Split and place it in the same folder.

Find additional details about the dataset in the of the Proposed split.

Training and Testing

If you want to skip the demo and just run training and testing for different dataset splits use:

python --dataset SUN --dataset_path xlsa17/data/ --alpha 3 --gamma 1

Setting the hyperparameters alpha and gamma is optional. If the values are not given, the code will evaluate on the train and validation set to find the best hyperparameters.


This version does not have the kernel implementation used in the paper. Hence the results fluctuate by a margin of 1-4%.

The results are taken from the paper Zero-Shot Learning - A Comprehensive Evaluation of the Good, the Bad and the Ugly and are evaluated for features extracted from ResNet-50 for the Proposed split.

Dataset Paper - (top-1 accuracy in %) Respository Results Hyper-params(trainval & test)
CUB 53.9 51.31 Alpha=2, Gamma=0
AWA1 58.2 56.19 Alpha=3, Gamma=0
AWA2 58.6 54.50 Alpha=3, Gamma=0
aPY 38.3 38.47 Alpha=3, Gamma=-1
SUN 54.5 55.62 Alpha=2, Gamma=2


If this repository was useful for your research, please cite.

  author = {Bharadwaj, Shrisha},
  title = {embarrsingly-simple-zero-shot-learning},
  year = {2018},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{}},
You can’t perform that action at this time.